Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Where's that phone?: geolocating IP addresses on 3G networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Towards street-level client-independent IP geolocation
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Octant: a comprehensive framework for the geolocalization of internet hosts
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Estimating the number of users behind ip addresses for combating abusive traffic
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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To conduct a successful targeting campaign in mobile advertising, one needs to have reliable location information from real-time bid requests. However, many real-time bid requests do not include fine-grained location information (such as latitude and longitude) because (1) the device or the application did not collect that information or (2) some components of the real-time bid ecosystem did not forward that information. In this paper, we present a three-step approach that takes as input hashed public IP addresses in real-time bid requests and (1) creates a weighted heterogenous network, (2) applies network-inference techniques to infer fine-grain (but possibly noisy) location information for the hashed public IPs, and (3) uses k-nearest neighbor and census data to assign census block group IDs to those hashed public IPs. Our experiments on two large real-world datasets show the accuracy of our approach to be over 74% for hashed IPs (regardless of their type: mobile or non-mobile) when basing the inference on only hashed public mobile IPs. This is notable since our inference is over 212K possibilities.